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        <p>来自官方程序</p>
<h3 id="基本概念"><a href="#基本概念" class="headerlink" title="基本概念"></a>基本概念</h3><p>使用 TensorFlow, 你必须明白 TensorFlow:</p>
<ul>
<li>使用图 (graph) 来表示计算任务.</li>
<li>在被称之为 <code>会话 (Session)</code> 的上下文 (context) 中执行图.</li>
<li>使用 tensor 表示数据.</li>
<li>通过 <code>变量 (Variable)</code> 维护状态.</li>
<li>使用 feed 和 fetch 可以为任意的操作(arbitrary operation) 赋值或者从其中获取数据.</li>
</ul>
<h3 id="使用常量"><a href="#使用常量" class="headerlink" title="使用常量"></a>使用常量</h3><pre><code>import tensorflow as tf

# 创建一个常量 op, 产生一个 1x2 矩阵. 这个 op 被作为一个节点
# 加到默认图中.
#
# 构造器的返回值代表该常量 op 的返回值.
matrix1 = tf.constant([[3., 3.]])

# 创建另外一个常量 op, 产生一个 2x1 矩阵.
matrix2 = tf.constant([[2.],[2.]])

# 创建一个矩阵乘法 matmul op , 把 &apos;matrix1&apos; 和 &apos;matrix2&apos; 作为输入.
# 返回值 &apos;product&apos; 代表矩阵乘法的结果.
product = tf.matmul(matrix1, matrix2)
</code></pre><h3 id="使用session"><a href="#使用session" class="headerlink" title="使用session"></a>使用session</h3><pre><code>#默认图现在有三个节点, 两个 constant() op, 和一个matmul() op. 为了真正进行矩阵相乘运算, 并得到矩阵乘法的 结果, 你必须在会话里启动这个图.
# 启动默认图.
sess = tf.Session()

# 调用 sess 的 &apos;run()&apos; 方法来执行矩阵乘法 op, 传入 &apos;product&apos; 作为该方法的参数. 
# 上面提到, &apos;product&apos; 代表了矩阵乘法 op 的输出, 传入它是向方法表明, 我们希望取回
# 矩阵乘法 op 的输出.
#
# 整个执行过程是自动化的, 会话负责传递 op 所需的全部输入. op 通常是并发执行的.
# 
# 函数调用 &apos;run(product)&apos; 触发了图中三个 op (两个常量 op 和一个矩阵乘法 op) 的执行.
#
# 返回值 &apos;result&apos; 是一个 numpy `ndarray` 对象.
result = sess.run(product)
print(result)
# ==&gt; [[ 12.]]

# 任务完成, 关闭会话.
sess.close()
</code></pre><p><code>Session</code> 对象在使用完后需要关闭以释放资源. 除了显式调用 close 外, 也可以使用 “with” 代码块 来自动完成关闭动作.</p>
<pre><code>with tf.Session() as sess:
  result = sess.run([product])
  print(result)
</code></pre><p>推荐使用with。</p>
<h3 id="指定计算设备"><a href="#指定计算设备" class="headerlink" title="指定计算设备"></a>指定计算设备</h3><p>如果机器上有超过一个可用的 GPU, 除第一个外的其它 GPU 默认是不参与计算的. 为了让 TensorFlow 使用这些 GPU, 你必须将 op 明确指派给它们执行. <code>with...Device</code> 语句用来指派特定的 CPU 或 GPU 执行操作:</p>
<pre><code>with tf.Session() as sess:
  with tf.device(&quot;/gpu:1&quot;):
    matrix1 = tf.constant([[3., 3.]])
    matrix2 = tf.constant([[2.],[2.]])
    product = tf.matmul(matrix1, matrix2)
</code></pre><p>设备用字符串进行标识. 目前支持的设备包括:</p>
<ul>
<li><code>&quot;/cpu:0&quot;</code>: 机器的 CPU.</li>
<li><code>&quot;/gpu:0&quot;</code>: 机器的第一个 GPU, 如果有的话.</li>
<li><code>&quot;/gpu:1&quot;</code>: 机器的第二个 GPU, 以此类推.</li>
</ul>
<p><a href="http://www.tensorfly.cn/tfdoc/how_tos/using_gpu.html" target="_blank" rel="noopener">使用GPU设备</a></p>
<h3 id="交互式使用session"><a href="#交互式使用session" class="headerlink" title="交互式使用session"></a>交互式使用session</h3><p>为了便于使用诸如 <a href="http://ipython.org/" target="_blank" rel="noopener">IPython</a> 之类的 Python 交互环境, 可以使用 <a href="http://www.tensorfly.cn/tfdoc/api_docs/python/client.html#InteractiveSession" target="_blank" rel="noopener"><code>InteractiveSession</code></a> 代替 <code>Session</code> 类, 使用 <a href="http://www.tensorfly.cn/tfdoc/api_docs/python/framework.html#Tensor.eval" target="_blank" rel="noopener"><code>Tensor.eval()</code></a> 和 <a href="http://www.tensorfly.cn/tfdoc/api_docs/python/framework.html#Operation.run" target="_blank" rel="noopener"><code>Operation.run()</code></a> 方法代替 <code>Session.run()</code>. 这样可以避免使用一个变量来持有会话.</p>
<pre><code># 进入一个交互式 TensorFlow 会话.
import tensorflow as tf
sess = tf.InteractiveSession()

x = tf.Variable([1.0, 2.0])
a = tf.constant([3.0, 3.0])

# 使用初始化器 initializer op 的 run() 方法初始化 &apos;x&apos; 
x.initializer.run()

# 增加一个减法 sub op, 从 &apos;x&apos; 减去 &apos;a&apos;. 运行减法 op, 输出结果 
sub = tf.sub(x, a)
print(sub.eval())
# ==&gt; [-2. -1.]
</code></pre><h3 id="使用变量"><a href="#使用变量" class="headerlink" title="使用变量"></a>使用变量</h3><pre><code># 创建一个变量, 初始化为标量 0.
state = tf.Variable(0, name=&quot;counter&quot;)

# 创建一个 op, 其作用是使 state 增加 1

one = tf.constant(1)
new_value = tf.add(state, one)
update = tf.assign(state, new_value)

# 启动图后, 变量必须先经过`初始化` (init) op 初始化,
# 首先必须增加一个`初始化` op 到图中.
</code></pre>
      
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  <script type="text/javascript">
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          proceedsearch();
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